Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks

• Published in: Minerals
• Main Authors: Juliani, Cyril Jerome, Ellefmo, Steinar Løve
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI 2019
• Subjects: Norway; Finnmark; Northern Norway
• Online Access: http://hdl.handle.net/11250/2587743; https://doi.org/10.3390/min9020131

In this paper, the radial basis function neural network (RBFNN) is used to generate a prospectivity map for undiscovered copper-rich (Cu) deposits in the Finnmark region, northern Norway. To generate the input data for RBFNN, geological and geophysical data, including up to 86 known mineral occurrences hosted in mafic host-rocks, were combined at different resolutions. Mineral occurrences were integrated into “deposit” and “non-deposit” training sets. Running RBFNN on different input vectors, with a k-fold cross-validation method, showed that increasing the number of iterations and radial basis functions resulted in: (1) a reduction of training mean squared error (MSE) down to 0.1, depending on the grid resolution, and (2) reaching correct classification rates of 0.9 and 0.6 for training and validation, respectively. The latter depends on: (1) the selection of “non-deposit” training data throughout the study area, (2) the scale at which data was acquired, and (3) the dissimilarity of input vectors. The “deposit” input data were correctly identified by the trained model (up to 83%) after proceeding to classification of non-training data. Up to 885 km2 of the Finnmark region studied is favorable for Cu mineralization based on the resulting mineral prospectivity map. The prospectivity map can be used as a reconnaissance guide for future detailed ground surveys. Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks publishedVersion © The Authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).